Object tracking is a low level computer graphics and has been extensively investigated in the past two and half decades. Object tracking has applications in numerous fields, such as video editing, video conferencing, video indexing, vehicle navigation etc. In different frames of a video a given object changes its position both absolutely and relative to other objects, and becomes subject to different lighting and shadow conditions. There are numerous challenges in accurately tracking an object in a video including: noise in the video frames, complex object motion and shape, occlusion, and changes in illumination.
Several approaches to object tracking have been suggested. Some that are relevant to these teachings are described in the following documents: [1] OBJECT TRACKING: A SURVEY, ACM Computing Surveys, Vol. 38, No. 4, Article 13, December 2006 by Alper Yilmaz, Omar Javed, and Mubarak Shah; [2] AN ITERATIVE IMAGE REGISTRATION TECHNIQUE WITH AN APPLICATION TO STEREO VISION, Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI), Volume 2, pp. 674-679, April 1981 by Bruce D. Lucas and Takeo Kanade; [3] DETECTION AND TRACKING OF POINT FEATURES, Computer Science Department, Carnegie Mellon University, pp. 1-20, April 1991 by C. Tomasi and T. Kanade; [4] GOOD FEATURES TO TRACK, IEEE Conference on Computer Vision and Pattern Recognition (CVPR94), June 1994, pp. 593-600, by J. Shi and C. Tomasi; [5] A COMBINED CORNER AND EDGE DETECTOR, Proceedings of the 4th Alvey Vision Conference, pp. 147-151, August 1988 by C. Harris and M. Stephens; [6] THE ESTIMATION OF THE GRADIENT OF A DENSITY FUNCTION, WITH APPLICATIONS IN PATTERN RECOGNITION, IEEE Transactions on Information Theory, Vol. 21, No. 1, pp. 32-40, January 1975 by K. Fukunaga and L. Hostetler; [7] REAL TIME FACE AND OBJECT TRACKING AS A COMPONENT OF A PERCEPTUAL USER INTERFACE, Fourth IEEE Workshop on Applications of Computer Vision, pages 214-219, October 1998 by G. R. Bradski; [8] MEAN SHIFT: A ROBUST APPROACH TOWARD FEATURE SPACE ANALYSIS, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 24, No. 5, pp. 603-619, May 2002 by Dorin Comaniciu and Peter Meer; [9] KERNEL-BASED OBJECT TRACKING, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25 No. 5, pp. 564-577, May 2003 by D. Comaniciu, V. Ramesh, and P. Meer; [10] ROBUST MEAN-SHIFT TRACKING WITH CORRECTED BACKGROUND-WEIGHTED HISTOGRAM., JET Computer Vision, Vol. 6, No. 1, pp. 62-69, January 2012 by J. Ning, L. Zhang, D. Zhang, and C. Wu; [11] MEAN-SHIFT BLOB TRACKING WITH ADAPTIVE FEATURE SELECTION AND SCALE ADAPTATION, IEEE International Conference on Image Processing, pages 369-372, September 2007 by Dawei Liang, Qingming Huang, Shugiang Jiang, Hongxun Yao, and Wen Gao; [12] ROBUST SCALE ADAPTIVE MEAN-SHIFT FOR TRACKING, Scandinavian Conference on Image Analysis, Vol. 7944, pp. 652-663, Springer Berlin Heidelberg, 2013 by Tomas Vojir, Jana Noskova, and Jiri Matas; [13] WATERSHEDS IN DIGITAL SPACES: AN EFFICIENT ALGORITHM BASED ON IMMERSION SIMULATIONS, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 13, No. 6, pp. 583-598, June 1991 by Luc Vincent and Pierre Soille; [14] AN ALGORITHM FOR FINDING BEST MATCHES IN LOGARITHMIC EXPECTED TIME, ACM Trans. Math. Softw., Vol. 3, No. 3, pp. 209-226, September 1977 by H. Friedman, Jerome, Jon Louis Bentley, and Raphael An Finkel; [15] REAL-TIME TRACKING OF NON-RIGID OBJECTS USING MEAN SHIFT, IEEE Conference on Computer Vision and Pattern Recognition, pp. 142-149, June 2000 by D. Comaniciu, V. Ramesh, and P. Meer; [16] REVIEW AND IMPROVEMENT AREAS OF MEAN SHIFT TRACKING ALGORITHM, The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014), pages 132-133, June 2014 by Kalyan Kumar Hati and A Vishnu Vardhanan; [17] FAST TEMPLATE MATCHING, Vision Interface, pp. 120-123, 1995 by J. P. Lewis; [18] THE DIVERGENCE AND BHATTACHARYYA DISTANCE MEASURES IN SIGNAL SELECTION, IEEE Transactions on Communication Technology, Vol. 15, No. 1, pp 52-60, February 1967 T. Kailath.
Two of the most promising approaches from the references above are the Kanade Lucas Tomashi (KLT) and mean shift (MS) tracker. The KLT tracker, described in documents [2-4], estimates the link motion vectors in the successive frames to track each Harris and Stephens corner points (document [5]) in a given region. The MS tracker described in document [8] finds the most similar candidate based on a histogram similarity function using the selected region of interest around the target location given by the user.
Generally, MS tracking involves both accurate detection of an object's center, and adaptation to a change in the scale of the object. Much work has been done on the former issue with some promising results, but there has not been much success on the scale adaptation issue. Generally, current approaches can accurately find the object's center position, but often fail when object scaling is present. Therefore, there is a need for an object tracking approach that can detect an object in a video with minimal user input and accurately track the object throughout a video.